• 1st week: Introduction to R and RStudio
• 2nd week: Data structure
• 3rd week: Graph I
• 4th week: Data manipulation I
• 5th week: Data manipulation II
• 6th week: Graph II
• 7th week: RMarkdown
• 8th week: Midterm exam

• 9th week: Basic statistics I
• 10th week: Basic statistics II
• 11th week: Graph III
• 12th week: Artificial Intelligence
• 13th week: Presentation of Project
• 14th week: Git
• 15th week: Github
• 16th week: Final exam

• A language and environment for statistical computing and graphics
• Allow the user to program algorithms and use libraries programmed by others

• Cool Graphics!
• Easy access to uptodate statistical methods

• Reproducible research
• Life will be easier

https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages

https://stackoverflow.blog/2017/10/10/impressive-growth-r/

• GUI for R

• Free!

• Easy to use

• R
  1. Go to https://cran.r-project.org
  2. Download binaries
  3. Install add-on packages
• RStudio
  1. Go to https://www.rstudio.com/products/rstudio/download/
  2. Get Open source edition

• Make new file> file
• Comment/Uncomment> code
• Set working directory> session

• packages> tools

• Make RMarkdown file: later

for i in [1, 2, 3, 4, 5]:
  print(i)

## 1
## 2
## 3
## 4
## 5

pwd
python --version

## /Users/kwangyeolpark/Dropbox/WorkingWithMyself/강의/SW중심대학/2018_1Q2Q_lecture
## Python 2.7.10

1 + 3

## [1] 4

a <- c(100, 234, 356, 477, 888)
mean(a)

## [1] 411

sd(a)

## [1] 301.2308

qplot(wt, mpg, data = mtcars)

ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point(aes(color=factor(gear))) + facet_wrap( ~ cyl)

demo(graphics)

CityPopularity$Mean=mean(CityPopularity$Popularity)
CC <- gvisComboChart(CityPopularity, xvar='City',
                     yvar=c('Mean', 'Popularity'),
                     options=list(seriesType='bars',
                                  width=450, height=300,
                                  title='City Popularity',
                                  series='{0: {type:"line"}}'))
plot(CC)

• Collecting and cleaning data
  • MS-EXCEL or MS-ACCESS
  • SPSS
  • txt file (CSV, comma-separated values)
• Analysis
  • SPSS or SAS
  • R
• Writing
  • MS-WORD
  • LATEX
  • HTML

• Modification of data
  • Addition of new data
  • Error correction in dataset after analysis

• The connection between dataset and tables/graph might be broken easily.

• Writing methods section based on the analysis you did 3 months ago.

• Repetitive analyses are boring!

Hi Dr. Park,

I have starting working on GPBB manuscript (ASH as well).

I need a paragraph from you describing the statistical methodology you used when you analyzed the data for the ISC abstract a few years ago.

Can you also send me the list of final study cohort (300 patients) to me?

Thanks,

• Cleaning data + Analysis + Writing

• Combining tool
  • R (and Rstudio) + LaTeX or Markdown
• Output
  • PDF, HTML, MS-WORD